Sleep quality data collection and evaluation

ABSTRACT

A sleep quality assessment approach involves collecting data based on detected physiological or non-physiological patient conditions. At least one of detecting patient conditions and collecting data is performed using an implantable device. Sleep quality may be evaluated using the collected data by an imlantable or patient-external sleep quality processor. One approach to sleep quality evaluation involves computing one or more summary metrics based on occurrences of movement disorders or breathing disorders during sleep.

FIELD OF THE INVENTION

The present invention relates generally to collecting and evaluatinginformation related to sleep quality.

BACKGROUND OF THE INVENTION

Sleep is generally beneficial and restorative to a patient, exertinggreat influence on the quality of life. The human sleep/wake cyclegenerally conforms to a circadian rhythm that is regulated by abiological clock. Regular periods of sleep enable the body and mind torejuvenate and rebuild. The body may perform various tasks during sleep,such as organizing long term memory, integrating new information, andrenewing tissue and other body structures.

Normal sleep is characterized by a general decrease in metabolic rate,body temperature, blood pressure, breathing rate, heart rate, cardiacoutput, sympathetic nervous activity, and other physiological functions.However, studies have shown that the brain's activity does not decreasesignificantly during sleep. Normally a patient alternates between rapideye movement (REM) and non-REM (NREM) sleep in approximately 90 minutecycles throughout a sleep period. A typical eight hour sleep period maybe characterized in terms of a five-step sleep cycle identifiablethrough EEG brain wave activity.

Non-REM sleep includes four sleep states or stages that range from lightdozing to deep sleep. Throughout NREM sleep, muscle activity is stillfunctional, breathing is low, and brain activity is minimal.Approximately 85% of the sleep cycle is spent in NREM sleep. Stage 1NREM sleep may be considered a transition stage between wakefulness andsleep. As sleep progresses to stage 2 NREM sleep, eye movements becomeless frequent and brain waves increase in amplitude and decrease infrequency. As sleep becomes progressively deeper, the patient becomesmore difficult to arouse. Stage 3 sleep is characterized by 20 to 40%slow brain wave (delta) sleep as detected by an electroencephalogram(EEG). Sleep stages 3 and 4 are considered to be the most restful sleepstages.

REM sleep is associated with more prevalent dreaming, rapid eyemovements, muscle paralysis, and irregular breathing, body temperature,heart rate and blood pressure. Brain wave activity during REM sleep issimilar to brain wave activity during a state of wakefulness. There aretypically 4-6 REM periods per night, with increasing duration andintensity toward morning. While dreams can occur during either REM orNREM sleep, the nature of the dreams varies depending on the type ofsleep. REM sleep dreams tend to be more vivid and emotionally intensethan NREM sleep dreams. Furthermore, autonomic nervous system activityis dramatically altered when REM sleep is initiated.

Lack of sleep and/or decreased sleep quality may be have a number ofcausal factors including, e.g., nerve or muscle disorders, respiratorydisturbances, and emotional conditions, such as depression and anxiety.Chronic, long-term sleep-related disorders e.g., chronic insomnia,sleep-disordered breathing, and sleep movement disorders, includingrestless leg syndrome (RLS), periodic limb movement disorder (PLMD) andbruxism, may significantly affect a patient's sleep quality and qualityof life.

Movement disorders such as restless leg syndrome (RLS), and a relatedcondition, denoted periodic limb movement disorder (PLMD), are emergingas one of the more common sleep disorders, especially among olderpatients. Restless leg syndrome is a disorder causing unpleasantcrawling, prickling, or tingling sensations in the legs and feet and anurge to move them for relief. RLS leads to constant leg movement duringthe day and insomnia or fragmented sleep at night. Severe RLS is mostcommon in elderly people, although symptoms may develop at any age. Insome cases, it may be linked to other conditions such as anemia,pregnancy, or diabetes.

Many RLS patients also have periodic limb movement disorder (PLMD), adisorder that causes repetitive jerking movements of the limbs,especially the legs. These movements occur approximately every 20 to 40seconds and cause repeated arousals and severely fragmented sleep.

A significant percentage of patients between 30 and 60 years experiencesome symptoms of disordered breathing, primarily during periods ofsleep. Sleep disordered breathing is associated with excessive daytimesleepiness, systemic hypertension, increased risk of stroke, angina andmyocardial infarction. Disturbed respiration can be particularly seriousfor patients concurrently suffering from cardiovascular deficiencies.Disordered breathing is particularly prevalent among congestive heartfailure patients, and may contribute to the progression of heartfailure.

Sleep apnea is a fairly common breathing disorder characterized byperiods of interrupted breathing experienced during sleep. Sleep apneais typically classified based on its etiology. One type of sleep apnea,denoted obstructive sleep apnea, occurs when the patient's airway isobstructed by the collapse of soft tissue in the rear of the throat.Central sleep apnea is caused by a derangement of the central nervoussystem control of respiration. The patient ceases to breathe whencontrol signals from the brain to the respiratory muscles are absent orinterrupted. Mixed apnea is a combination of the central and obstructiveapnea types. Regardless of the type of apnea, people experiencing anapnea event stop breathing for a period of time. The cessation ofbreathing may occur repeatedly during sleep, sometimes hundreds of timesa night and occasionally for a minute or longer.

In addition to apnea, other types of disordered respiration have beenidentified, including, for example, hypopnea (shallow breathing),dyspnea (labored breathing), hyperpnea (deep breathing), and tachypnea(rapid breathing).

Combinations of the disordered respiratory events described above havealso been observed. For example, Cheyne-Stokes respiration (CSR) isassociated with rhythmic increases and decreases in tidal volume causedby alternating periods of hyperpnea followed by apnea and/or hypopnea.The breathing interruptions of CSR may be associated with central apnea,or may be obstructive in nature. CSR is frequently observed in patientswith congestive heart failure (CHF) and is associated with an increasedrisk of accelerated CHF progression.

An adequate duration and quality of sleep is required to maintainphysiological homeostasis. Untreated, sleep disturbances may have anumber of adverse health and quality of life consequences ranging fromhigh blood pressure and other cardiovascular disorders to cognitiveimpairment, headaches, degradation of social and work-relatedactivities, and increased risk of automobile and other accidents.

The following commonly owned U.S. patents applications, some of whichhave been identified above, are hereby incorporated by reference intheir respective entireties: U.S. patent application Ser. No. 10/309,770(Docket Number GUID.064PA), filed Dec. 4, 2002, U.S. patent applicationSer. No. 10/309,771 (Docket Number GUID.054PA), filed Dec. 4, 2002, U.S.patent application entitled “Prediction of Disordered Breathing,”identified by Docket Number GUID.088PA and concurrently filed with thispatent application, U.S. patent application entitled “Adaptive Therapyfor Disordered Breathing,” identified by Docket Number GUID.059PA andfiled concurrently with this patent application, U.S. patent applicationentitled “Sleep State Classification,” identified by Docket NumberGUID.060PA and filed concurrently with this patent application, and U.S.patent application entitled “Therapy Triggered by Prediction ofDisordered Breathing,” identified by Docket Number GUID.103PA and filedconcurrently with this patent application.

Various modifications and additions can be made to the preferredembodiments discussed hereinabove without departing from the scope ofthe present invention. Accordingly, the scope of the present inventionshould not be limited by the particular embodiments described above, butshould be defined only by the claims set forth below and equivalentsthereof.

SUMMARY OF THE INVENTION

Various embodiments of present invention involve methods and systems forcollecting sleep quality data and evaluating the sleep quality of apatient.

An embodiment of the invention involves a method for collecting sleepquality data. The method includes detecting physiological andnon-physiological conditions associated with the sleep quality of apatient and collecting sleep quality data based on the detectedconditions. Collecting the sleep quality data is performed at least inpart implantably.

Another embodiment of the invention involves a method for evaluatingsleep quality. In accordance with this method, one or more metricsassociated with sleep are determined. One or more metrics associatedwith events that disrupt sleep are determined. A composite sleep qualitymetric is determined using the one or more metrics associated with sleepand the one or more metrics associated with events that disrupt sleep.

In yet another embodiment of the invention, a method for evaluatingsleep quality includes detecting physiological and non-physiologicalconditions associated with the sleep quality of a patient and collectingsleep quality data based on the detected conditions. The sleep qualityof the patient is evaluated using the collected data. At least one ofcollecting the sleep quality data and evaluating the sleep quality ofthe patient is performed at least in part implantably.

Another embodiment of the invention involves a method for evaluatingsleep quality. One or more conditions associated with sleep quality of apatient are detected during a period of wakefulness. Sleep quality datais collected based on the detected conditions. The patient's sleepquality is evaluated using the collected sleep quality data. At leastone of collecting the data and evaluating the sleep quality is performedat least in part implantably.

A further embodiment of the invention involves a medical deviceincluding a detector system configured to detect physiological andnon-physiological conditions associated with sleep quality and a datacollection system for collecting sleep quality data based on thedetected conditions. The data collection system includes an implantablecomponent.

Yet another embodiment of the invention relates to a medical deviceconfigured to evaluate sleep quality. The medical device includes adetector system configured to detect physiological and non-physiologicalconditions associated with the sleep quality of a patient. A sleepquality processor, coupled to the detection system, is configured todetermine metrics based on the detected conditions. The metrics includeone or more metrics associated with sleep, one or more metricsassociated with events that disrupt sleep, and at least one compositesleep quality metric based on the one or more metrics associated withsleep and the one or more metrics associated with events that disruptsleep.

In another embodiment of the invention, a medical device for assessingsleep quality includes a detector unit configured to detectphysiological and non-physiological conditions associated with sleepquality and a sleep quality data collection unit configured to collectsleep quality data based on the detected conditions. A data analysisunit coupled to the data collection unit evaluates sleep quality basedon the collected sleep quality data. At least one of the data collectionunit and the data analysis unit includes an implantable component.

A further embodiment of the invention involves a system for collectingsleep quality data. The system includes means for detectingphysiological and non-physiological conditions associated with sleepquality and means for collecting sleep quality data based on thedetected conditions. The means for collecting the sleep quality dataincludes an implantable component.

In yet another embodiment of the invention, a system for assessing sleepquality includes means for determining one or more metrics associatedwith sleep and means for determining one or more metrics associated withevents that disrupt sleep. The system further includes means fordetermining a composite sleep quality metric as a function of themetrics associated with sleep and the metrics associated with eventsthat disrupt sleep.

Another embodiment of the invention involves a system for evaluatingsleep quality. The system includes means for detecting physiological andnon-physiological conditions associated with sleep quality and means forcollecting sleep quality data based on the detected conditions. Thesystem includes means for evaluating the sleep quality of the patientbased on the collected sleep quality data. At least one of the means forcollecting the sleep quality data and the means for evaluating the sleepquality comprise an implantable component.

A further embodiment involves a system for evaluating the sleep qualityof a patient. The system includes means for detecting one or morepatient conditions associated with sleep quality during a period ofwakefulness and means for collecting sleep quality data based on thedetected conditions. The system further includes means for evaluatingthe sleep quality of the patient using the collected sleep quality data.At least one of the means for collecting the sleep quality data andmeans for evaluating the sleep quality include an implantable component.

The above summary of the present invention is not intended to describeeach embodiment or every implementation of the present invention.Advantages and attainments, together with a more complete understandingof the invention, will become apparent and appreciated by referring tothe following detailed description and claims taken in conjunction withthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow chart illustrating a method of collecting sleep qualitydata in accordance with embodiments of the invention;

FIG. 2 illustrates a block diagram of a sleep quality data system inaccordance with embodiments of the invention;

FIG. 3 is a system block diagram illustrating a sleep quality datasystem incorporated within a cardiac rhythm management system inaccordance with embodiments of the invention;

FIG. 4 is a block diagram of a sleep detection unit that may be used aspart of a sleep quality data system according to embodiments of theinvention;

FIG. 5 is a flow chart illustrating a sleep detection method accordingto embodiments of the invention;

FIG. 6 is a flow chart illustrating a method for detecting sleep as apart of a sleep quality data collection approach according toembodiments of the invention;

FIG. 7 is a graph of a patient's activity condition as indicated by anaccelerometer signal;

FIG. 8 is a graph of a patient's heart rate;

FIG. 9 is a graph of a patient's minute ventilation (MV) condition;

FIG. 10 illustrates adjustment of a sleep threshold in accordance withembodiments of the invention;

FIG. 11 illustrates a normal respiration pattern as represented by atransthoracic impedance sensor signal;

FIG. 12 illustrates respiration intervals used for disordered breathingdetection according to embodiments of the invention;

FIG. 13 illustrates detection of sleep apnea and severe sleep apneaaccording to embodiments of the invention;

FIGS. 14A-B are graphs of tidal volume derived from transthoracicimpedance measurements according to embodiments of the invention;

FIG. 15 is a flow chart illustrating a method of apnea and hypopneadetection according to embodiments of the invention;

FIG. 16 is a graph illustrating a breathing interval according toembodiments of the invention;

FIG. 17 is a graph illustrating a hypopnea detection approach inaccordance with embodiments of the invention;

FIGS. 18A-B are charts illustrating nomenclature for individualdisordered breathing events and combinations of disordered breathingevents, respectively;

FIGS. 18C-G are graphs illustrating disordered breathing eventscomprising a combination of apnea and hypopnea respiration cycles;

FIG. 19 is a flow chart of a method for detecting disordered breathingby classifying breathing patterns in accordance with embodiments of theinvention; and

FIG. 20 illustrates a patient instrumented with components of a sleepquality data system according to embodiments of the invention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail below. It is to be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the invention isintended to cover all modifications, equivalents, and alternativesfalling within the scope of the invention as defined by the appendedclaims.

DETAILED DESCRIPTION OF VARIOUS EMBODIMENTS

In the following description of the illustrated embodiments, referencesare made to the accompanying drawings which form a part hereof, and inwhich are shown, by way of illustration, various embodiments by whichthe invention may be practiced. It is to be understood that otherembodiments may be utilized. Structural and functional changes may bemade without departing from the scope of the present invention.

Sleep quality assessments depend upon acquiring sleep-related data,including the patient's typical sleep patterns and the physiological,environmental, contextual, emotional, and other conditions affecting thepatient during sleep. Diagnosis of sleep disorders and assessment ofsleep quality often involves the use of a polysomnographic sleep studyat a dedicated sleep facility. However, such studies are costly,inconvenient to the patient, and may not accurately represent thepatient's typical sleep behavior. In a polysomnographic sleep study, thepatient is instrumented for data acquisition and observed by trainedpersonnel. Sleep assessment in a laboratory setting presents a number ofobstacles in acquiring an accurate picture of a patient's typical sleeppatterns. For example, spending a night in a sleep laboratory typicallycauses a patient to experience a condition known as “first nightsyndrome,” involving disrupted sleep during the first few nights in anunfamiliar location. In addition, sleeping while instrumented andobserved may not result in a realistic perspective of the patient'snormal sleep patterns.

Further, polysomnographic sleep studies provide an incomplete data setfor the analysis of some sleep disorders, including, for example, sleepdisordered breathing. A number of physiological conditions associatedwith sleep disordered breathing are detectable during periods ofwakefulness, e.g., decreased heart rate variability, elevatedsympathetic nerve activity, norepinephrine concentration, and increasedblood pressure variability. Collection of data during periods of sleepand/or during periods of wakefulness may provide a more complete pictureof the patient's sleep quality.

Various aspects of sleep quality, including number and severity ofarousals, sleep disordered breathing episodes, nocturnal limb movements,and cardiac, respiratory, muscle, and nervous system functioning mayprovide important information for diagnosis and/or therapy delivery. Aninitial step to sleep quality evaluation is an accurate and reliablemethod for discriminating between periods of sleep and periods ofwakefulness. Further, acquiring data regarding the patient's sleepstates or stages, including sleep onset, termination, REM, and NREMsleep states may be used in connection sleep quality assessment. Forexample, the most restful sleep occurs during stages 3 and 4 NREM sleep.One indicator of sleep quality is the percentage of time a patientspends in these sleep stages. Knowledge of the patient's sleep patternsmay be used to diagnose sleep disorders and/or adjust patient therapy,including, e.g., cardiac or respiratory therapy. Trending disorderedbreathing episodes, arousal episodes, and other sleep quality aspectsmay be helpful in determining and maintaining appropriate therapies forpatients suffering from disorders ranging from snoring to chronic heartfailure.

The present invention involves methods and systems for acquiring sleepquality data using one or more implantable components. As illustrated inFIG. 1, methods of the invention involve detecting conditions associatedwith the sleep quality of the patient 110, including physiological andnon-physiological conditions. Data related to the patient's sleepquality is collected based on the detected conditions 120. Detection ofpatient conditions related to sleep quality may occur during periods ofwakefulness and/or during periods of sleep. Either detecting theconditions associated with sleep quality, or collecting the sleepquality data, or both, is performed using a device having a componentthat is at least in part implantable.

A representative set of the conditions associated with sleep quality islisted in Table 1. Patient conditions used to evaluate sleep quality mayinclude, for example, both physiological and non-physiological (i.e.,contextual) conditions. Physiological conditions associated with sleepquality may be further organized, for example, into conditions of thecardiovascular, respiratory, muscle, and nervous systems, and conditionsrelating to the patient's blood chemistry.

Contextual conditions may be further subdivided into environmentalconditions, body-related conditions and historical/backgroundconditions. Environmental conditions may be broadly defined to includethe environmental surroundings affecting the patient, such as ambientlight, temperature, humidity, air pollution, noise, and barometricpressure. Body-related conditions may include, for example, patientlocation, posture, and altitude. Contextual conditions relevant to sleepquality may also include historical or background conditions. Forexample, a patient's medical/psychological history, gender, age, weight,body mass index, neck size, drug use, and emotional state may bedetected and used in connection with sleep quality evaluation and sleepdisorder diagnosis. Methods and systems for detecting contextualconditions are described in commonly owned U.S. patent application, Ser.No. 10/269611, filed Oct. 11, 2002, which is incorporated herein byreference. TABLE 1 Condition Type Condition Sensor type or Detectionmethod Physiological Cardiovascular Heart rate EGM, ECG System Heartrate variability QT interval Ventricular filling pressure Intracardiacpressure sensor Blood pressure Blood pressure sensor Respiratory SystemSnoring Accelerometer Microphone Respiration pattern Transthoracicimpedance sensor (Tidal volume Minute (AC) ventilation Respiratory rate)Patency of upper airway Intrathoracic impedance sensor Pulmonarycongestion Transthoracic impedance sensor (DC) Nervous SystemSympathetic nerve activity Muscle sympathetic nerve Activity sensorBrain activity EEG Blood Chemistry CO2 saturation Blood analysis O2saturation Blood alcohol content Adrenalin Brain Natriuretic Peptide(BNP) C-Reactive Protein Drug/Medication/Tobacco use Muscle SystemMuscle atonia EMG Eye movement EOG Patient activity Accelerometer, MV,etc. Limb movements Accelerometer Jaw movements Non- EnvironmentalAmbient temperature Thermometer physiological Humidity HygrometerPollution Air quality website Time Clock Barometric pressure BarometerAmbient noise Microphone Ambient light Photodetector Body-relatedPosture Posture sensor Altitude Altimeter Location GPS, proximity sensorProximity to bed Proximity to bed sensor Historical/ Historical sleeptime Patient input, previously Background detected sleep onset timesMedical history Patient input device Age Recent exercise Weight GenderBody mass index Neck size Emotional state Psychological history Daytimesleepiness Patient perception of sleep quality Drug, alcohol, nicotineuse

Each of the conditions listed in Table 1 may serve a variety of purposesin evaluating sleep quality. For example, a subset of the conditions maybe used to detect whether the patient is asleep and to track the variousstages of sleep and arousal incidents. Another subset of the conditionsmay be used to detect disordered breathing episodes. Yet another subsetmay be used to detect abnormal limb movements. In one implementation,some or all of the listed conditions may be collected over a relativelylong period of time and used to analyze long term sleep quality trends.Trending may be used in connection with an overall assessment of sleepquality and diagnosis and treatment of sleep-disordered breathing,movement disorders, and/or other sleep disorders.

In one implementation, sleep quality analysis may be used within thestructure of an advanced patient management system. In thisimplementation, an advanced patient management system having sleepquality analysis capability allows a physician to remotely andautomatically monitor cardiac and respiratory functions, as well asother patient conditions, including information related to sleepquality. In one example, an implantable cardiac rhythm managementsystem, such as a cardiac monitor, pacemaker, defibrillator, orresynchronization device, may be equipped with varioustelecommunications and information technologies to enable real-time datacollection, diagnosis, and treatment of the patient. Systems and methodsinvolving advanced patient management techniques are described in U.S.Pat. Nos. 6,336,903, 6,312,378, 6,270,457, and 6,398,728 which areincorporated herein by reference in their respective entireties.

Table 2 provides examples of how some physiological andnon-physiological conditions may be used in connection with sleepquality assessment. TABLE 2 Examples of how condition is Condition TypeCondition used in sleep quality assessment Physiological Heart rateDecrease in heart rate may indicate disordered breathing episode.Decrease in heart rate may indicate the patient is asleep. Heart ratevariability May be used to determine sleep state. Changes in heart ratevariability, detected during periods of sleep or wakefulness, mayindicate that the patient suffers from sleep disordered breathing. QTinterval May be used to detect sleep apnea. Ventricular filling pressureMay be used to identify/predict pulmonary congestion associated withrespiratory disturbance. Blood pressure Variation in blood pressure isassociated with apnea. Snoring Associated with a higher incidence ofobstructive sleep apnea and may be used to detect disordered breathing.Respiration pattern May be used to detect disordered breathing episodes.May be used to determine the type of disordered breathing. May be usedto detect sleep. Patency of upper airway Related to obstructive sleepapnea and may be used to detect episodes of obstructive sleep apnea.Pulmonary congestion Associated with respiratory disturbances.Sympathetic nerve activity Apnea termination is associated with a spikein SNA. (SNA) SNA activity may be elevated during periods of wakefulnessif the patient experiences sleep disordered breathing.Electroencephalogram May be used to determine sleep stages, includingREM and (EEG) NREM sleep stages CO2 saturation Low CO2 levels mayindicate initiation of central apnea. O2 saturation O2 desaturationoccurs during severe apnea/hypopnea episodes. Blood alcohol contentAlcohol tends to increase the incidence of snoring & obstructive apnea.Adrenalin End of apnea associated with a spike in blood adrenaline.Brain Natriuretic Peptide A marker of heart failure status, which isassociated with (BNP) Cheyne-Stokes Respiration. C-Reactive Protein Ameasure of inflammation that may be related to apnea.Drug/Medication/Tobacco These substances may affect incidence of bothcentral & use obstructive apnea. Muscle atonia Muscle atonia may be usedin connection with detection of REM and non-REM sleep. Eye movement Eyemovement may be used in connection with detection of REM and non-REMsleep. Activity May be used to detect sleep and patient well being. Limbmovements May be used to detect abnormal limb movements during sleep.Non-physiological Ambient Temperature Ambient temperature may predisposethe patient to episodes of disordered breathing during sleep. HumidityHumidity may predispose the patient to episodes of disordered breathingduring sleep. Pollution Pollution may predispose the patient to episodesof disordered breathing during sleep. Posture Posture may be used todetermine if the patient is asleep. Posture may predispose the patientto disordered breathing. Time Used to establish historical sleep time.Ambient noise level Noise level may affect sleep quality. LocationPatient location may used to determine if the patient is in bed as apart of sleep detection. Altitude Altitude may predispose the patient toepisodes of disordered breathing and may affect sleep quality.Barometric Pressure Barometric pressure may predispose the patient toepisodes of disordered breathing. Proximity to bed May be used todetermine if patient is in bed. Historical sleep time May be used inconnection with sleep detection. Medical history History of medicaldisorders, e.g., CHF, that are associated with disordered breathing suchas Cheyne-Stokes respiration. Age Age is associated with increased riskof disordered breathing, RLS and other sleep disruptive disorders.Weight Associated with sleep disordered breathing, e.g., obstructiveGender sleep apnea. Obesity Neck size Patient reported drug, Patientdrug, alcohol and nicotine use may affect sleep alcohol, nicotine usequality. Psychological history Psychological factors, e.g., clinicaldepression may be associated with insomnia. Emotional state Emotionalstate, e.g., stress, anxiety, euphoria, may affect sleep quality.Daytime sleepiness May be used to evaluate sleep quality. Patientperceptions of sleep quality

FIG. 2 illustrates a block diagram of a sleep quality data system 200configured in accordance with embodiments of the invention. In thisimplementation, the sleep quality data system 200 may use signalsacquired from a variety of sources to collect data relevant to sleepquality. The collected data may be stored in the memory 280 of a datacollection unit 250 and/or transmitted to a sleep quality analysis unit290 for further processing. It is contemplated that at least one, some,or all components of the system 200 are implanted in the patient.

The sleep quality data system 200 may use patient-internal sensors 210implanted within the body of the patient to detect physiologicalconditions relevant to sleep quality. The conditions detected usingpatient-internal sensors 210 may include, for example, heart rate,respiratory pattern, and patient activity.

The system 200 may also use patient-external sensors 220 to detectphysiological or non-physiological patient conditions. In one exampleconfiguration, whether the patient is snoring may be useful inevaluating sleep quality. Snoring data may be detected using an externalmicrophone and transferred to the sleep quality data collection unit250. In another configuration, ambient temperature and humidity may befactors related to the patient's sleep quality. The ambient temperatureand humidity of the patient's room may be sensed using sensors locatednear patient. Signals from the temperature and humidity sensors may betransmitted to the data collection unit 250. Limb and/or jaw movementsmay be sensed using patient-external accelerometers and/or other sensorsplaced in appropriate locations on or near the patient and transmittedto the data collection unit 250.

Information relevant to sleep quality may also be reported 240 by thepatient. According to embodiments of the invention, the patient'sself-described conditions, including medication use, tobacco use,perceptions of sleep quality, and/or psychological or emotional state,for example, may be relevant to sleep quality assessment. The patientmay enter information about these conditions through an appropriateinterface device, such as a medical device programmer, coupled to thesleep quality data collection unit 250.

Some information related to sleep quality may be accessible throughinformation systems 230, including network-based systems. For example,information about the patient's present cardiac, respiratory, or othertherapy may be downloaded from an external device via a wireless orwired network. In another example, information about conditionsaffecting the patient, such as local air quality data, may be accessedthrough an internet-connected website.

The patient-internal sensors 210, patient-external sensors 220,patient-reported input devices 240, and information systems 230, may becoupled to the data collection unit 250 in a variety of ways. In oneexample, one or more of the sensors 210, 220, patient-reported inputdevices 240, and information systems 230 have wireless communicationcapabilities, such as a wireless Bluetooth communications link, or otherproprietary communications protocol. In this implementation, the deviceshaving wireless communication capabilities may remotely transmit signalsto the data collection unit 250. In this application, the datacollection unit 250 may be configured as an implantable orpatient-external device. In other implementations, one or more of thepatient-internal sensors 210, patient-external sensors 220,patient-reported input devices 240, and information systems 230 may becoupled to the data collection unit 250 through leads or other wiredconnections.

The implantable or patient-external data collection unit 250 includesdetection circuitry 260 for processing signals from the sensors 210,220, patient-reported input devices 240, and information systems 230.The detection circuitry 260 may include, for example, amplifiers,filters, A/D converters, signal processors and/or sensor drivercircuitry configured to provide sensor signals used in the evaluation ofsleep quality. The data collection unit 250 may further include wirelesscommunication circuitry 270 for receiving and transmitting signals towirelessly connected components.

In one embodiment, the sleep quality data system 200 collects data fromthe sensors 210, 220, input devices 240, and information systems 230,and stores the collected data in memory 280 prior to further processingor transmission. In another embodiment, the sleep quality data system200 may transmit the collected data to a separate device (not shown) forstorage, analysis, or display.

In a further embodiment, the sleep quality data system 200 may evaluateor further process the collected sleep quality data. For this purpose,the sleep quality data system 200 may optionally include a sleep qualityanalysis unit 290. In one configuration, the data collection unit 250and the sleep quality analysis unit 290 are arranged in separatedevices. In such a configuration, the data collection unit 250 transfersthe collected sleep quality data to the sleep quality analysis unit 290through a wireless or wired connection. In another configuration, thesleep quality analysis unit 290 and the data collection unit 250 arearranged within the same device which may be a patient-external deviceor a fully or partially implantable device.

The sleep quality analysis unit 290 may include one or more subsystemsuseful in sleep quality assessment. The subsystems may include, forexample a sleep detector 292 used to detect sleep onset, sleep offset,and arousal, for example. The sleep detector may also detect sleepstages, including the various stages of NREM and REM sleep. The sleepquality analysis unit 290 may include circuitry to detect varioussleep-related disorders. For example, the sleep quality analysis unit290 may include circuitry 294 for detecting disordered breathing andcircuitry 295 for detecting abnormal nocturnal movements. Further, thesleep quality analysis unit 290 may include a processor for evaluatingsleep quality 296, for example, by calculating one or more metricsquantifying the patient's sleep quality.

FIG. 3 illustrates a sleep quality data system 305 incorporated within acardiac rhythm management system (CRM) 300. The CRM 300 may include, forexample, a cardiac therapy module 320 including a pacemaker 322 and anarrhythmia detector/therapy unit 324. The cardiac therapy module 320 iscoupled to a lead system having electrodes 331 implantable within thepatient's body. The implanted electrodes 331 are arranged to receivesignals from the heart 330 and deliver stimulation therapy to the heart330. Cardiac signals sensed using the implanted electrodes 331 arereceived by a cardiac signal detector 350 coupled to the cardiac therapymodule 320.

The cardiac therapy module 320 analyzes the cardiac signals to determinean appropriate therapy to treat arrhythmia conditions affecting theheart 330. The cardiac therapy may include pacing therapy controlled bythe pacemaker 322 to treat cardiac rhythms that are too slow. Thepacemaker 322 controls the delivery of periodic low energy pacing pulsesto ensure that the periodic contractions of the heart are maintained ata hemodynamically sufficient rate.

The cardiac therapy may also include therapy to terminatetachyarrhythmia, wherein the heart rate is too fast. The arrhythmiadetector/therapy unit 324 analyzes cardiac signals received from thecardiac signal detector 350 to recognize tachyarrhythmias includingatrial or ventricular tachycardia or fibrillation. The arrhythmiadetector/therapy unit 324 recognizes cardiac signals indicative oftachyarrhythmia and delivers high energy stimulations to the heart 330through the implanted electrodes 331 to terminate the arrhythmias.

Various input devices, including implantable sensors 381,patient-external sensors 382, patient input devices 384, and informationsystems 383 may be coupled to the CRM 300. These devices 381, 382, 383,384 may be used to provide information about physiological and/ornon-physiological conditions affecting the patient relevant to sleepquality, such as the representative set of patient conditions listed inTable 1 above.

The CRM 300 includes signal detection circuitry 360 for receiving andprocessing signals from the various sensors and input devices 381, 382,384, 383. As previously discussed, the signal detection circuitry 360may include signal processing circuitry configured to amplify, digitize,or otherwise process signals representing the sensed sleep qualityconditions. In the illustrated implementation, the patient input devices384, patient-external sensors 382, and information systems 383 arewirelessly coupled to the CRM 300. The patient-internal sensors 381 maybe coupled to the CRM 300 through leads, through a wireless link, orintegrated within or on the housing of the CRM 300 (e.g., integralaccelerometer).

In one embodiment, the sleep quality data system 305 incorporated withinthe CRM 300 collects data from cardiac electrodes 331, patient-internalsensors 381, patient-external sensors 382, patient input devices 384,and information systems 383 and stores the collected data in memory. Thesleep quality data system may transmit the collected data to a separatedevice, such as the CRM programmer 315 or other device, periodically asrequired or desired.

In another embodiment, the CRM sleep quality data system 305 may performfurther processing and/or evaluation of the sleep quality data. For thispurpose, the CRM sleep quality data system 305 may include a sleepquality analysis unit 340 coupled to the signal detector 360. The sleepquality analysis unit 340 may include one or more components forevaluating the patient's sleep quality. For example, the sleep qualityanalysis unit 340 may include sleep detection circuitry 341, disorderedbreathing detection circuitry 342, abnormal nocturnal movement detectioncircuitry 344, and a sleep quality processor 343, as previouslydescribed in connection with FIG. 2.

The cardiac therapy module 320, signal detector 360, and sleep qualityanalysis unit 340 operate in cooperation with a memory unit 370. Thememory unit 370 may store parameters associated with cardiac therapy inaddition to diagnostic or other data related to cardiac functioning andsleep quality. A communication unit 310 located within the CRM 300 maybe used to transmit programming information and collected data from theCRM 300 to an external device such as a programmer 315.

Sleep quality assessment involves a reliable method for discriminatingbetween a state of sleep and a state of wakefulness. One method ofdetecting sleep involves comparing one or more detected physiologicalconditions to thresholds indicative of sleep. When the detectedconditions are consistent with thresholds indicating sleep, then sleeponset is declared. For example, decreased patient activity is acondition associated with sleep. When the patient's activity falls belowa predetermined threshold, the system declares the onset of sleep. Whenthe patient's activity rises above the threshold, the system declaresthe end of sleep. In a similar manner, a number of patient conditions,such as heart rate, respiration rate, brain wave activity, etc., may becompared individually or collectively compared to thresholds or otherindices to detect sleep.

An enhanced method of sleep detection is described in commonly ownedU.S. patent application, Ser. No. 10/309,771, filed Dec. 4, 2002, whichis incorporated herein by reference. The method involves adjusting asleep threshold associated with a first patient condition using a secondpatient condition. The first patient condition is compared to theadjusted threshold to determine if the patient is asleep or awake.

FIG. 4 is a block diagram of a sleep detection unit 400 that may be usedas part of a sleep quality data system according to embodiments of theinvention. The sleep detection unit 400 uses a number of sensors 401,402, 403 to sense sleep-related patient conditions. A representative setof sleep-related conditions include, for example, patient activity,patient location, posture, heart rate, QT interval, eye movement,respiration rate, transthoracic impedance, tidal volume, minuteventilation, brain activity, muscle tone, body temperature, time of day,and blood oxygen level.

According to embodiments of the invention, a first sleep-relatedcondition detected using a sleep detection sensor 401 is compared to asleep threshold for detecting the onset and termination of sleep. Asecond sleep-related condition, detected using a threshold adjustmentsensor 402, is used to adjust the sleep threshold. Although the exampledescribed herein involves one sleep detection sensor 401 and onethreshold adjustment sensor 402, any number of thresholds or otherindices corresponding to a number of sleep detection sensors may beused. Furthermore, conditions detected using any number of adjustmentsensors may be used to adjust the thresholds or indices of a pluralityof sleep detection signals. Additional sleep-related signals derivedfrom one or more confirmation sensors 403 may optionally be used toconfirm the onset or termination of the sleep condition.

Signals derived from the sensors 401, 402, 403 are received by a sensordriver/detection circuitry 410 that may include, for example,amplifiers, signal processing circuitry, and/or A/D conversion circuitryfor processing each sensor signal. The sensor driver/detection system410 may further include sensor drive circuitry required to activate thesensors 401, 402, 403.

The sensor signal detection system 410 is coupled to a sleep detector430. The sleep detector 430 is configured to compare the level of afirst sleep-related condition detected using the sleep detection sensor401 to a sleep threshold adjusted by a second sleep-related conditiondetected using the threshold adjustment sensor 402. A determination ofsleep onset or sleep termination may be made by the sleep detector 430based on the comparison. The onset or termination of sleep mayoptionally be confirmed using patient conditions derived using a sleepconfirmation sensor 403.

FIG. 5 is a flow chart illustrating a method of detecting sleep used ina sleep quality data system configured according to principles of theinvention. A sleep threshold associated with a first sleep-relatedpatient condition is established 505. The sleep threshold may bedetermined from clinical data of a sleep threshold acquired using agroup of subjects, for example. The sleep threshold may also bedetermined using historical data taken from the particular patient forwhom the sleep condition is to be detected.

First and second sleep-related conditions are detected 510, 520. Thefirst and the second sleep-related conditions may be detected usingsensors implanted in the patient, attached externally to the patient orlocated remote from the patient, for example, as previously described inconnection with FIG. 3. The first and the second sleep-relatedconditions may include any condition associated with sleep and are notlimited to the representative sleep-related conditions listed above.

The sleep threshold established for the first sleep-related condition isadjusted using the second sleep-related condition 530. For example, ifthe second sleep-related condition indicates a high level of activitythat is incompatible with a sleep state, the sleep threshold of thefirst sleep-related condition may be adjusted downward to requiresensing a decreased level of the first sleep-related condition before asleep condition is detected.

If the first sleep-related condition is consistent with sleep accordingto the adjusted sleep threshold 540, sleep is detected 550. If the firstsleep-related condition is not consistent with sleep using the adjustedsleep threshold, the first and the second sleep-related conditionscontinue to be detected 510, 520 and the threshold adjusted 530 untilsleep is detected 550.

The flow chart of FIG. 6 illustrates a method for detecting sleep usingaccelerometer and MV signals according to embodiments of the invention.In the method illustrated in FIG. 6, an accelerometer and a minuteventilation sensor are used to detect patient activity and patientrespiration conditions, respectively. A preliminary sleep threshold isdetermined 610 with respect to the patient activity condition sensed bythe accelerometer. The preliminary sleep threshold may be determinedfrom clinical data derived from a group of subjects or from historicaldata taken from the patient over a period of time.

The activity condition of the patient is monitored 620 using anaccelerometer that may be incorporated in an implantable cardiac rhythmmanagement system as described in connection with FIG. 3. Alternatively,the accelerometer may be attached externally to the patient. Thepatient's MV condition is monitored 625, for example, using atransthoracic impedance sensor. A transthoracic impedance sensor may beimplemented as a component of an implantable CRM device.

In this embodiment, the patient's activity represents the sleepdetection condition and is compared to the sleep threshold. Thepatient's MV is used as the threshold adjustment condition to adjust thesleep threshold. In addition, in this example, the patient's heart rateis monitored 630 and used to provide a sleep confirmation condition.

The sleep threshold adjustment is accomplished using the patient's MVcondition to adjust the activity sleep threshold. If the patient's MVcondition is low relative to an expected MV level associated with sleep,the activity sleep threshold is increased. Similarly, if the patient'sMV level is high relative to an expected MV level associated with sleep,the activity sleep threshold is decreased. Thus, when the patient's MVlevel is high, less activity is required to make the determination thatthe patient is sleeping. Conversely when the patient's MV level isrelatively low, a higher activity level may result in detection ofsleep. The use of two sleep-related conditions to determine thepatient's sleep state enhances the accuracy of sleep detection overprevious methods.

Various signal processing techniques may be employed to process the rawsensor signals. For example, a moving average of a plurality of samplesof the sensor signals may be calculated. Furthermore, the sensor signalsmay be amplified, filtered, digitized or otherwise processed.

If the MV level is high 635 relative to an expected MV level associatedwith sleep, the activity sleep threshold is decreased 640. If the MVlevel is low 635 relative to an expected MV level associated with sleep,the activity sleep threshold is increased 645.

If the patient's activity level is less than or equal to the adjustedsleep threshold 650, and if the patient is currently in a sleep state665, then the patient's heart rate is checked 680 to confirm that thepatient is asleep. If the patient's heart rate is compatible with sleep680, then sleep onset is determined 690. If the patient's heart rate isincompatible with sleep, then the patient's sleep-related conditionscontinue to be monitored.

If the patient's activity level is less than or equal to the adjustedsleep threshold 650 and if the patient is currently in a sleep state665, then a continuing sleep state is determined and the patient'ssleep-related conditions continue to be monitored for sleep terminationto occur.

If the patient's activity level is greater than the adjusted sleepthreshold 650 and the patient is not currently in a sleep state 660,then the patient's sleep-related conditions continue to be monitoreduntil sleep onset is detected 690. If the activity level is greater thanthe adjusted sleep threshold 650 and the patient is currently in a sleepstate 660, then sleep termination is detected 670.

The graphs of FIGS. 7-10 illustrate the adjustment of the activity sleepthreshold. The relationship between patient activity and theaccelerometer and MV signals is trended over a period of time todetermine relative signal levels associated with sleep. The graph ofFIG. 7 illustrates the patient's activity as indicated by anaccelerometer. The patient's heart rate for the same period is shown inthe graph of FIG. 8. The accelerometer signal indicates a period ofsleep associated with a relatively low level of activity beginningslightly before 23:00 and continuing through 6:00. The patient's heartrate appropriately tracks the activity level indicated by theaccelerometer indicating a similar period of decreased heart ratecorresponding to sleep. The signal level of the accelerometer duringknown periods of sleep may be used to establish a threshold for sleepdetection.

FIG. 9 is a graph of the patient's minute ventilation signal over time.Historical data of minute ventilation is graphed over an 8 month period.The minute ventilation data may be used to determine the minuteventilation signal level associated with sleep. In this example, acomposite minute ventilation graph using the historical data presents aroughly sinusoidal shape with the relatively low minute ventilationlevels occurring during the period approximately from hours 21:00through 8:00. The decreased minute ventilation level is associated withperiods of sleep. The minute ventilation level associated with sleep isused to implement sleep threshold adjustment.

FIG. 10 illustrates adjustment of the activity sleep threshold using theMV data. The initial sleep threshold 1010 is established using thebaseline activity data acquired as discussed above. If the patient's MVlevel is low relative to an expected MV level associated with sleep, theactivity sleep threshold is increased 1020. If the patient's MV level ishigh relative to an expected MV level associated with sleep, theactivity sleep threshold is decreased 1030. When the patient's MV levelis high, less activity detected by the accelerometer is required to makethe determination that the patient is sleeping. However, if thepatient's MV level is relatively low, a higher activity level may resultin detection of sleep. The use of two sleep-related signals to establishand adjust a sleep threshold enhances the accuracy of sleep detectionover previous methods.

Additional sleep-related conditions may be sensed and used to improvethe sleep detection method described above. For example, a posturesensor may be used to detect the posture of the patient and used toconfirm sleep. If the posture sensor signal indicates an uprightposture, then the posture sensor signal may be used to override adetermination of sleep using the sleep detection and thresholdadjustment conditions. Other conditions may also be used in connectionwith sleep determination or confirmation, including the representativeset of sleep-related conditions indicated above. In another example, aproximity to bed sensor may be used alone or in combination with aposture sensor to detect that the patient is in bed and is lying down.

A sleep detection system may detect sleep onset, termination, arousalsas well as the sleep stages, including REM and non-REM sleep. REM sleepmay be discriminated from NREM sleep, for example, by examining one ormore signals indicative of REM, e.g., muscle atonia, rapid eyemovements, or EEG signals. Methods and systems for detecting REM sleepthat are particularly useful for patients with implantable devices arediscussed in commonly owned U.S. patent application identified underDocket Number GUID.060PA, and entitled “Sleep State Classification,”concurrently filed with the present application, and incorporated hereinby reference. Various conditions indicative of sleep state may bedetected using sensors, e.g., electroencephalogram (EEG),electrooculogram (EOG), or electromyogram (EMG) sensors, coupled throughwired or wireless connections to the sleep detection circuitry. Thesleep detection circuitry may analyze the various patient conditionssensed by the sensors to track the patient's sleep through various sleepstates, including REM and NREM stages.

Returning to FIG. 3, the sleep quality data system 300 may also employdisordered breathing detection circuitry 342 to detect episodes ofdisordered breathing. Disordered breathing may be detected in numerousways using one or more of the patient conditions listed in Table 1.Methods and systems for detecting disordered breathing are described incommonly owned U.S. patent application, Ser. No. 10/309,770, filed Dec.4, 2002, which is incorporated herein by reference. According to thisapproach, disordered breathing may be detected by examiningcharacteristics of the patient's respiration patterns to determine ifthe respiration patterns are consistent with disordered breathing.

FIG. 11 illustrates a normal respiration pattern as represented by atransthoracic impedance sensor signal. The transthoracic impedanceincreases during respiratory inspiration and decreases duringrespiratory expiration. During NREM sleep, a normal respiration patternincludes regular, rhythmic inspiration—expiration cycles withoutsubstantial interruptions.

In one embodiment, detection of disordered breathing, including, forexample, sleep apnea and hypopnea, involves defining and examining anumber of respiratory cycle intervals. FIG. 12 illustrates respirationintervals used for disordered breathing detection according to anembodiment of the invention. A respiration cycle is divided into aninspiration period corresponding to the patient inhaling, an expirationperiod, corresponding to the patient exhaling, and a non-breathingperiod occurring between inhaling and exhaling. Respiration intervalsare established using inspiration 1210 and expiration 1220 thresholds.The inspiration threshold 1210 marks the beginning of an inspirationperiod 1230 and is determined by the transthoracic impedance signalrising above the inspiration threshold 1210. The inspiration period 1230ends when the transthoracic impedance signal is maximum 1240. A maximumtransthoracic impedance signal 1240 corresponds to both the end of theinspiration interval 1230 and the beginning of the expiration interval1250. The expiration interval 1250 continues until the transthoracicimpedance falls below an expiration threshold 1220. A non-breathinginterval 1260 starts from the end of the expiration period 1250 andcontinues until the beginning of the next inspiration period 1270.

Detection of sleep apnea and severe sleep apnea according to embodimentsof the invention is illustrated in FIG. 13. The patient's respirationsignals are monitored and the respiration cycles are defined accordingto inspiration 1330, expiration 1350, and non-breathing 1360 intervalsas described in connection with FIG. 12. A condition of sleep apnea isdetected when a non-breathing period 1360 exceeds a first predeterminedinterval 1390, denoted the sleep apnea interval. A condition of severesleep apnea is detected when the non-breathing period 1360 exceeds asecond predetermined interval 1395, denoted the severe sleep apneainterval. For example, sleep apnea may be detected when thenon-breathing interval exceeds about 10 seconds, and severe sleep apneamay be detected when the non-breathing interval exceeds about 20seconds.

Hypopnea is a condition of disordered breathing characterized byabnormally shallow breathing. FIGS. 14A-B are graphs of respirationpatterns derived from transthoracic impedance measurements. The graphscompare the tidal volume of a normal breathing cycle to the tidal volumeof a hypopnea episode. FIG. 14A illustrates normal respiration tidalvolume and rate. As shown in FIG. 14B, hypopnea involves a period ofabnormally shallow respiration.

According to an embodiment of the invention, hypopnea is detected bycomparing a patient's respiratory tidal volume to a hypopnea tidalvolume threshold. The tidal volume for each respiration cycle may bederived from transthoracic impedance measurements. The hypopnea tidalvolume threshold may be established using clinical results providing arepresentative tidal volume and duration for hypopnea events. In oneconfiguration, hypopnea is detected when an average of the patient'srespiratory tidal volume taken over a selected time interval falls belowthe hypopnea tidal volume threshold.

FIG. 15 is a flow chart illustrating a method of apnea and/or hypopneadetection according to embodiments of the invention. Various parametersare established 1501 before analyzing the patient's respiration fordisordered breathing episodes, including, for example, inspiration andexpiration thresholds, sleep apnea interval, severe sleep apneainterval, and hypopnea tidal volume threshold.

The patient's transthoracic impedance is detected 1505. If thetransthoracic impedance exceeds 1510 the inspiration threshold, thebeginning of an inspiration interval is detected 1515. If thetransthoracic impedance remains below 1510 the inspiration threshold,then the impedance signal is checked 1505 periodically until inspiration1515 occurs.

During the inspiration interval, the patient's transthoracic impedanceis monitored until a maximum value of the transthoracic impedance isdetected 1520. Detection of the maximum value signals an end of theinspiration period and a beginning of an expiration period 1535.

The expiration interval is characterized by decreasing transthoracicimpedance. When the transthoracic impedance falls below 1540 theexpiration threshold, a non-breathing interval is detected 1555.

If the transthoracic impedance does not exceed 1560 the inspirationthreshold within a first predetermined interval 1565, denoted the sleepapnea interval, then a condition of sleep apnea is detected 1570. Severesleep apnea is detected 1580 if the non-breathing period extends beyonda second predetermined interval 1575, denoted the severe sleep apneainterval.

When the transthoracic impedance exceeds 1560 the inspiration threshold,the tidal volume from the peak-to-peak transthoracic impedance iscalculated 1585. The peak-to-peak transthoracic impedance provides avalue proportional to the tidal volume of the respiration cycle. Thisvalue is compared 1590 to a hypopnea tidal volume threshold. If thepeak-to-peak transthoracic impedance is consistent with 1590 thehypopnea tidal volume threshold for a predetermined time 1592, then ahypopnea cycle is detected 1595.

Additional sensors, such as motion sensors and/or posture sensors, maybe used to confirm or verify the detection of a sleep apnea or hypopneaepisode. The additional sensors may be employed to prevent false ormissed detections of sleep apnea or hypopnea due to posture and/ormotion related artifacts.

Another embodiment of the invention involves classifying respirationpatterns as disordered breathing episodes based on the breath intervalsand/or tidal volumes of one or more respiration cycles within therespiration patterns. According to this embodiment, the duration andtidal volumes associated with a respiration pattern are compared toduration and tidal volume thresholds. The respiration pattern may bedetermined to represent a disordered breathing episode based on thecomparison.

According to this embodiment, a breath interval 1630 is established foreach respiration cycle. A breath interval represents the interval oftime between successive breaths, as illustrated in FIG. 16. A breathinterval 1630 may be defined in a variety of ways, for example, as theinterval of time between successive maxima 1610, 1620 of the impedancesignal waveform.

Detection of disordered breathing, in accordance with methods of theinvention, involves the establishment of a duration threshold and atidal volume threshold. If a breath interval exceeds the durationthreshold, an apnea event is detected. Detection of sleep apnea, inaccordance with this embodiment, is illustrated in the graph of FIG. 16.Apnea represents a period of non-breathing. A breath interval 1630exceeding a duration threshold 1640 comprises an apnea episode.

Hypopnea may be detected using a duration threshold and a tidal volumethreshold. A hypopnea event represents a period of shallow breathinggreater than the duration threshold. Each respiration cycle in ahypopnea event is characterized by a tidal volume less than the tidalvolume threshold. Further, the decreased tidal volume cycles persistlonger than the duration threshold.

A hypopnea detection approach, in accordance with embodiments of theinvention, is illustrated in FIG. 17. Shallow breathing is detected whenthe tidal volume of one or more breaths is below a tidal volumethreshold 1710. If the shallow breathing continues for an intervalgreater than a duration threshold 1720, then the breathing patternrepresented by the sequence of shallow respiration cycles, is classifiedas a hypopnea event.

FIGS. 18A and 18B provide charts illustrating classification ofindividual disordered breathing events and combination of periodicbreathing events, respectively. As illustrated in FIG. 18A, individualdisordered breathing events may be grouped into apnea, hypopnea,tachypnea and other disordered breathing events. Apnea events arecharacterized by an absence of breathing. Intervals of reducedrespiration are classified as hypopnea events. Tachypnea events includeintervals of rapid respiration characterized by an elevated respirationrate.

As illustrated in FIG. 18A, apnea and hypopnea events may be furthersubdivided as either central events, e.g., caused either by centralnervous system dysfunction, or obstructive events, e.g., caused by upperairway obstruction. A tachypnea event may be further classified as ahyperpnea event, represented by rapid deep breathing (hyperventilation).A tachypnea event may alternatively be classified as rapid shallowbreathing, typically of prolonged duration.

FIG. 18B illustrates classification of periodic disordered breathingevents. Periodic breathing may be classified as obstructive, central ormixed. Obstructive periodic breathing is characterized by cyclicrespiratory patterns with an obstructive apnea or hypopnea event in eachcycle. In central periodic breathing, the cyclic respiratory patternsinclude a central apnea or hypopnea event in each cycle. Periodicbreathing may also be of mixed origin. In this case, cyclic respiratorypatterns have a mixture of obstructive and central apnea events in eachcycle. Cheyne-Stokes respiration is a particular type of periodicbreathing characterized by a gradual waxing and waning of tidal volumeand having a central apnea and hyperpnea event in each cycle. Othermanifestations of periodic breathing are also possible.

As illustrated in FIGS. 18C-G, a respiration pattern detected as adisordered breathing episode may include only an apnea respiration cycle1810 (FIG. 18C), only hypopnea respiration cycles 1850 (FIG. 18F), or amixture of hypopnea and apnea respiration cycles 1820 (FIG. 18D), 1830(FIG. 18E), 1860 (FIG. 18G). A disordered breathing event 1820 may beginwith an apnea respiration cycle and end with one or more hypopneacycles. In another pattern, the disordered breathing event 1830 maybegin with hypopnea cycles and end with an apnea cycle. In yet anotherpattern, a disordered breathing event 1860 may begin and end withhypopnea cycles with an apnea cycle in between the hypopnea cycles.Analysis of the characteristic respiration patterns associated withvarious types of disordered breathing may be used to detect, classifyand evaluate disordered breathing episodes.

FIG. 19 is a flow chart of a method for detecting disordered breathingby classifying breathing patterns using breath intervals in conjunctionwith tidal volume and duration thresholds as previously described above.In this example, a duration threshold and a tidal volume threshold areestablished for determining both apnea and hypopnea breath intervals. Anapnea episode is detected if the breath interval exceeds the durationthreshold. A hypopnea episode is detected if the tidal volume ofsuccessive breaths remains less than the tidal volume threshold for aperiod in excess of the duration threshold. Mixed apnea/hypopneaepisodes may also occur. In these cases, the period of disorderedbreathing is characterized by shallow breaths or non-breathingintervals. During the mixed apnea/hypopnea episodes, the tidal volume ofeach breath remains less than the tidal volume threshold for a periodexceeding the duration threshold.

The patient's respiration cycles are determined, for example, usingtransthoracic impedance signals. Each breath 1910 is characterized by abreath interval, i.e., the interval of time between two impedance signalmaxima and a tidal volume (TV). If a breath interval exceeds 1915 theduration threshold, then the respiration pattern is consistent with anapnea event, and an apnea event trigger is turned on 1920. If the tidalvolume of the breath interval exceeds 1925 the tidal volume threshold,then the breathing pattern is characterized by two respiration cycles ofnormal volume separated by a non-breathing interval. This patternrepresents a purely apneic disordered breathing event, and apnea isdetected 1930. Because the final breath of the breath interval wasnormal, the apnea event trigger is turned off 1932, signaling the end ofthe disordered breathing episode. However, if the tidal volume of thebreath interval does not exceed 1925 the tidal volume threshold, thedisordered breathing period is continuing and the next breath is checked1910.

If the breath interval does not exceed 1915 the duration threshold, thenthe tidal volume of the breath is checked 1935. If the tidal volume doesnot exceed 1935 the tidal volume threshold, the breathing pattern isconsistent with a hypopnea cycle and a hypopnea event trigger is set on1940. If the tidal volume exceeds the tidal volume threshold, then thebreath is normal.

If a period of disordered breathing is in progress, detection of anormal breath signals the end of the disordered breathing. If disorderedbreathing was previously detected 1945, and if the disordered breathingevent duration has not exceeded 1950 the duration threshold, and thecurrent breath is normal, then no disordered breathing event is detected1955. If disordered breathing was previously detected 1945, and if thedisordered breathing event duration has extended for a period of timeexceeding 1950 the duration threshold, and the current breath is normal,then the disordered breathing trigger is turned off 1960. In thissituation, the duration of the disordered breathing episode was ofsufficient duration to be classified as a disordered breathing episode.If an apnea event was previously triggered 1965, then an apnea event isdeclared 1970. If a hypopnea was previously triggered 1965, then ahypopnea event is declared 1975.

As previously discussed in connection with FIG. 3, a sleep qualityanalysis unit 340 may incorporate an abnormal nocturnal movementdetector 344 to evaluate the movements of a patient during the night todetect nocturnal movement disorders such as RLS, PLMD, and/or bruxism.The patient may be instrumented with accelerometers located on the limbsor jaw, for example, to sense patient movement. Excessive movement, ormovements having a characteristic pattern, e.g., periodic limb or jawmovements, may be classified as abnormal nocturnal movements. Forexample, bruxism is a sleep disorder wherein the patient grinds histeeth during sleep. An accelerometer attached to the patient's jaw maybe used to sense movement of the jaw. Signals from the jaw accelerometermay be transferred to the abnormal movement detector for evaluation todetermine if the movements are excessive or unusually periodic,indicating bruxism. In a similar application, accelerometers attached tothe patient's limbs may generate signals used by the abnormal movementdetector 344 to detect and classify disorders such as RLS and PLMD.

FIG. 20 illustrates a patient 2010 instrumented with a sleep qualitydata system 2000 according to embodiments of the invention. The sleepquality data system collects sleep quality data from the patient using anumber of sensors 2011-2019. In one configuration, the collected data isanalyzed by a sleep quality analysis unit that may be an integratedcomponent of an implantable sleep quality data collection and analysisunit 2020. In another configuration, the collected data may bedownloaded to a patient-external device 2030 for storage, analysis, ordisplay.

In the implementation illustrated in FIG. 20, the sleep quality datasystem 2000 includes an implantable sleep quality data collection andanalysis unit 2020 coupled to a number of sensors 2011-2019. In thisexample, the sensors include an EGM sensor 2016 for detecting heart rateand heart rate variability conditions. A transthoracic impedance sensor2017 is used to detect the respiration conditions of the patient,including, for example, minute ventilation, respiration rate, and tidalvolume. An activity detector, e.g., accelerometer, 2015 may be used todetect patient activity conditions. The sleep quality data systemdetects patient conditions including the patient's posture and locationusing a posture sensor 2014 and a proximity to bed sensor 2013,respectively. The sleep quality data system senses the patient's brainactivity using EEG sensors 2011 and the patient's eye movements usingEOG sensors 2012. Jaw and limb movements are sensed using accelerometersattached to the patient's jaw 2018 and legs 2019.

In this application, the sleep quality data collection and analysis unit2020 is configured to track the patient's heart rate, heart ratevariability, minute ventilation, respiration rate, tidal volume,posture, proximity to bed, brain activity, eye movements, jaw movementsand leg movements. At periodic intervals, the system samples signalsfrom the sensors and stores data regarding the detected conditions inmemory circuitry within the sleep quality data collection and analysisunit 2020. The sleep quality data collection and analysis unit 2020 mayadditionally access an external input unit 2030 to detect patientreported conditions, for example, recent tobacco and medication use bythe patient. Further, the sleep quality data collection and analysisunit 2020 may monitor conditions using one or more external sensors. Inthe illustrated example, a thermometer 2035 is coupled through theexternal programmer 2030 and a pollution website 2040 is accessible tothe sleep quality data collection and analysis unit 2020 through theinternet 2050.

The sleep quality data collection and analysis unit 2020 may operate toacquire data during periods of both sleep and wakefulness. It may bebeneficial, for example, to track changes in particular conditionsmeasured during periods of wakefulness that are associated with sleepdisordered breathing. For example, some patients who suffer from sleepapnea experience changes in heart rate variability, blood pressurevariability, and/or sympathetic nerve activity during periods ofwakefulness. Detection and analysis of the physiological changesattributable to sleep disorders and measurable during the time thepatient is awake provides a more complete picture of sleep quality.

In another example, the patient's sleep quality may be evaluated bydetermining the patient's activity level while the patient is awake. Theactivity level of the patient during the day may provide importantinformation regarding the patient's sleep quality. For example, if thepatient is very inactive during periods of wakefulness, this mayindicate that the patient's sleep is of inadequate quality or duration.Such information may also be used in connection with assessing theefficacy of a particular sleep disorder therapy and/or adjusting thepatient's sleep disorder therapy. Methods and systems for determiningthe patient's activity level and generally assessing the well-being of apatient are described in commonly owned U.S. Pat. No. 6,021,351 which isincorporated herein by reference.

The analysis unit 2020 may calculate one or more sleep quality metricsquantifying the patient's sleep quality. A representative set of thesleep quality metrics include, for example, sleep efficiency, sleepfragmentation, number of arousals per hour, denoted the arousal index(AI).

The analysis unit 2020 may also compute one or more metrics quantifyingthe patient's disordered breathing, such as the apnea hypopnea index(AHI) providing the number of apneas and hypopneas per hour, and thepercent time in periodic breathing (% PB).

Further, metrics associated with sleep movement disorders may also bedetermined by the analysis unit 2020. Such metrics may include, forexample, a general sleep movement disorder index (MDI) representing thenumber of abnormal movements arising from movement disorders such asrestless leg syndrome, periodic limb movement disorder and bruxism perhour. In addition, specific indices may be calculated for each type ofmovement disorder, e.g., a bruxism index (BI) characterizing the numberof jaw movements per hour, a RLS index (RLSI) characterizing the numberof restless leg syndrome episodes per hour, and a PLM index (PLMI)characterizing the number of periodic limb movements experienced by thepatient per hour.

In addition, percentage of sleep time during which the patientexperiences movement disorders (% MD) may be calculated. Specificmetrics relating to the percentage of time during which the patientexperiences bruxism (% B), restless leg syndrome (% RLS), and periodicleg movement disorder (% PLMD) may also be determined.

Further, sleep summary metrics may be computed, either directly from thecollected patient condition data, or by combining the above-listed sleepquality and sleep disorder metrics. In one embodiment, a composite sleepdisordered respiration metric (SDRM) may be computed by combining theapnea hypopnea index AHI and the arousal index AI. The composite sleepdisordered respiration metric (SDRM) may be computed as a linearcombination of the AHI and AI as follows:SDRM=c ₁ *AHI+c ₂ *AI  [1]where c₁ and c₂ are constants chosen to balance the relativecontributions of respiratory and arousal effects on sleep disturbance.The AHI may be monitored by performing disordered breathing detectionbased on transthoracic impedance measurements as previously described.The AI may be estimated, for example, by monitoring the patientactivity, minute ventilation, and posture sensors for body motionindicating sleep termination or arousal. A more sensitive measure ofarousal may be made using EEG signals. In this implementation, theconstant c₂ may be adjusted to reflect the increased sensitivity toarousal.

In another embodiment, an undisturbed respiration sleep time (URST) orundisturbed respiration sleep efficiency (URSE) may be computed based onthe amount of time the patient spends asleep in bed without respiratorydisturbance.

The URST or URSE metrics may be determined using three parameters: totaltime in bed (TIB), total time asleep (TA), and combined sleep timeduration in disturbed respiration (STDR). Time in bed may be determinedby a combination of posture sensing and sensing the proximity of thepatient to bed. The posture condition of the patient may determined, forexample, using an implantable multiaxis accelerometer sensor.

The patient's total time in bed (TIB) may be determined using aproximity to bed sensor. The proximity to bed sensor may use a receiverin the sleep quality data collection and analysis unit 2020 forreceiving signals transmitted from a beacon 2070 located at thepatient's bed 2060. If the proximity to bed receiver detects a signal ofsufficient strength from the proximity to bed beacon 2070, then thereceiver detects that the patient is in bed 2060.

Total time asleep (TA) may be determined using the sleep detectionmethod described in more detail above. The total sleep time in disturbedrespiration (STDR) may be determined, for example, based on detection ofsleep and disordered breathing using the sleep and disordered breathingdetection methods described above.

The patient's undisturbed respiration sleep time (URST) is calculatedas:URST=TA−STDR  [2]where TA=total time asleep and STDR=sleep time in disturbed breathing.

The undisturbed respiration sleep efficiency (USE) in percent iscalculatedURSE=100*URST/TIB  [3]

where URST=undisturbed respiration sleep time and TIB=total time in bed.

Similar metrics may be calculated for movement disorders generally, orfor specific movement disorders, e.g., RLS, PLMD, or bruxism. Forexample, the composite RLS, PLMD, and bruxism metrics, RLSM, PLMDM, andBM, respectively, may be calculated using equations similar in form toequation 1 above:RLSM=c ₁ *RLSI+c ₂ *AI  [4]

where RLSI=number of restless leg movement syndrome episodes per hour,AI=number of arousals per hour, and c₁ and c₂ are constants chosen tobalance the relative contributions of abnormal movement and arousaleffects on sleep disturbance.PLMDM=c ₁ *PLMDI+c ₂ *AI  [5]

where PLMDI=number of periodic leg movement syndrome episodes per hour,AI=number of arousals per hour, and c₁ and c₂ are constants chosen tobalance the relative contributions of abnormal movement and arousaleffects on sleep disturbance.BM=c ₁ *BMI+c ₂ *AI  [6]

where BMI=number of bruxism movement episodes per hour, AI=number ofarousals per hour, and c₁ and c₂ are constants chosen to balance therelative contributions of abnormal movement and arousal effects on sleepdisturbance.

The patient's undisturbed movement sleep time (UMST) and undisturbedmovement sleep efficiency (UMSE) may be calculated for each movementrelated disorder separately or in combination using equations similar inform to equations 2 and 3, above.

In addition, a composite sleep disorder index SDI quantifying thecombined effect of both respiratory and movement disorders may becomputed by combining the apnea hypopnea index (AHI), the movementdisorder index (MDI), and the arousal index (AI).

A sleep disturbance index (SDI) may be computed as a linear combinationof the AHI, and the combined disorder index DI_(c). The combineddisorder index may include both abnormal breathing and movementcomponents. For example, the sleep disturbance index SDI ischaracterizable by the equation:SDI=c ₄ *DI _(c) +c ₃ *AI,  [7]

where DI_(c) is a combined disorder index of the form:DI _(c) =c ₄₁ *DI ₁ +c ₄₂ *DI ₂  [7a]In equation 7, c₄ and c₃ are constants chosen to balance the relativecontributions of the combined disorder and arousal effects,respectively. The disorder index, DI_(c), may be used to characterizethe effects of one or more sleep disorders, including, e.g., disordersassociated with disturbed respiration and/or abnormal movements. Thecombined disorder index may represent only one disorder index, or may bea linear combination of two or more sleep disorder indices, e.g., theapnea/hypopnea index (AHI) and the abnormal movement disorder index(MDI). The constants C₄₁ and c₄₂ may be used as weighting factorsassociated with particular disorder indices.

The patient's undisturbed sleep time (UST) may be calculated:UST=TA−STSD  [8]

where TA=total time asleep and STSD=sleep time spent in sleep disorders.

The undisturbed sleep efficiency (USE) in percent may be calculated:USE=100*UST/TIB  [9]

where UST=undisturbed sleep time and TIB=total time in bed.

Sleep quality metrics, such as those described above, or other metrics,may be acquired and analyzed using the sleep quality data collection andanalysis unit 2020. Sleep quality metrics, in addition to raw orprocessed data based on physiological and non-physiological conditionsmay determined periodically, e.g., daily, and stored or transmitted toanother device. Such data can be presented to the patient's health careprofessional on a real-time basis, or as a long-term, e.g., month longor year long, trend of daily measurements.

The health care professional may access the data during clinic visitsvia programmer interrogation of the implanted device, through occasionalor periodic trans-telephonic device interrogations, or through anautomatic or “on-demand” basis in the context of an advanced patientmanagement system. The health care professionals may use the sleepquality indicator trends alone or in conjunction with otherdevice-gathered or clinical data to diagnose disorders and/or adjust thepatient's device or medical therapy as needed to improve the patient'squality of sleep.

The present invention provides diagnostic, monitoring, and evaluationcapabilities relating to sleep quality and may be particularly valuablein the context of an advanced patient management system. Undiagnosedsleep disorders can lead to increased morbidity and mortality, such asthose arising from various respiratory and cardiovascular consequences.Routine monitoring of patient sleep quality may lead to improveddiagnosis and treatment of these syndromes and their associatedco-morbidities. The invention may provide less obtrusive sleep qualitymonitoring, particularly and is suited for patients having an implanteddevice. The present invention serves to improve diagnosis of sleepdisorders by reducing the inconveniences, unnatural sleep environmentissues, and expenses associated with sleep clinic polysomnogram studies.

The following commonly owned U.S. patents applications, some of whichhave been identified above, are hereby incorporated by reference intheir respective entireties: U.S. patent application Ser. No. 10/309,770(Docket Number GUID.064PA), filed Dec. 4, 2002, U.S. patent applicationSer. No. 10/309,771 (Docket Number GUID.054PA), filed Dec. 4, 2002, U.S.patent application entitled “Prediction of Disordered Breathing,”identified by Docket Number GUID.088PA and concurrently filed with thispatent application, U.S. patent application entitled “Adaptive Therapyfor Disordered Breathing,” identified by Docket Number GUID.059PA andfiled concurrently with this patent application, U.S. patent applicationentitled “Prediction of Disordered Breathing,” identified by DocketNumber GUID.088PA and filed concurrently with this patent application,and U.S. patent application entitled “Therapy Triggered by Prediction ofDisordered Breathing,” identified by Docket Number GUID.103PA and filedconcurrently with this patent application.

Various modifications and additions can be made to the preferredembodiments discussed hereinabove without departing from the scope ofthe present invention. Accordingly, the scope of the present inventionshould not be limited by the particular embodiments described above, butshould be defined only by the claims set forth below and equivalentsthereof.

1. A method for collecting sleep quality data, comprising: detectingphysiological and non-physiological conditions associated with sleepquality of a patient; and collecting sleep quality data based on thedetected conditions, wherein collecting the sleep quality data isperformed at least in part implantably.
 2. The method of claim 1,wherein detecting the conditions comprises detecting a cardiovascularsystem condition.
 3. The method of claim 1, wherein detecting theconditions comprises detecting a respiratory system condition.
 4. Themethod of claim 1, wherein detecting the conditions comprises detectinga muscle system condition.
 5. The method of claim 1, wherein detectingthe conditions comprises detecting a blood chemistry condition.
 6. Themethod of claim 1, wherein detecting the conditions comprises detectinga nervous system condition.
 7. The method of claim 1, wherein detectingthe conditions comprises detecting an environmental condition.
 8. Themethod of claim 1, wherein detecting the conditions comprises detectinga contextual condition.
 9. The method of claim 1, wherein collecting thesleep quality data comprises collecting data associated with sleepstages.
 10. The method of claim 1, wherein collecting the sleep qualitydata comprises collecting data associated with sleep disruption.
 11. Themethod of claim 1, wherein collecting the sleep quality data comprisescollecting data associated with disordered breathing.
 12. The method ofclaim 1, wherein collecting the sleep quality data comprises collectingdata associated with a movement disorder.
 13. The method of claim 1,further comprising storing the collected sleep quality data.
 14. Themethod of claim 1, further comprising transmitting the collected sleepquality data.
 15. A method for assessing sleep quality, comprising:determining one or more metrics associated with sleep; determining oneor more metrics associated with events that disrupt sleep; anddetermining a composite sleep quality metric as a function of the one ormore metrics associated with sleep and the one or more metricsassociated with events that disrupt sleep.
 16. The method of claim 15,wherein at least one of determining the one or more metrics associatedwith sleep, determining the one or more metrics associated with eventsthat disrupt sleep, and determining the composite sleep quality metricis performed at least in part implantably.
 17. The method of claim 15,wherein determining the one or more metrics associated with sleepcomprises determining a metric characterizing arousals.
 18. The methodof claim 15, wherein determining the one or more metrics associated withsleep comprises determining a metric characterizing total time asleep.19. The method of claim 15, wherein determining the one or more metricsassociated with sleep comprises determining a metric characterizing timein bed.
 20. The method of claim 15, wherein determining the one or moremetrics associated with events that disrupt sleep comprises determiningone or more metrics associated with a sleep disorder.
 21. The method ofclaim 15, wherein determining the one or more metrics associated withevents that disrupt sleep comprises determining one or more metricsassociated with a movement disorder.
 22. The method of claim 15, whereindetermining the one or more metrics associated with events that disruptsleep comprises determining one or more metrics associated withdisordered breathing.
 23. The method of claim 15, wherein determiningthe one or more metrics associated with events that disrupt sleepcomprises determining a metric characterizing sleep time spent in one ormore sleep disorders.
 24. The method of claim 15, wherein determiningthe composite sleep quality metric comprises determining a sleepdisturbance index.
 25. The method of claim 24, wherein determining thesleep disturbance index comprises calculating a metric as a function ofa combination of one or more sleep disorder indices and an arousalindex.
 26. The method of claim 24, wherein determining the sleepdisturbance index comprises calculating a metric as a function of acombination of a movement disorder index and an arousal index.
 27. Themethod of claim 24, wherein determining the sleep disturbance metriccomprises calculating a metric as a function of a combination of adisordered breathing index and an arousal index.
 28. The method of claim24, wherein the sleep disturbance index is characterizable using theequation:SDI=c ₁ *DI+c ₂ *AI where DI is associated with a sleep disorder index,AI is associated with an arousal index, and c₁ and c₂ are constants. 29.The method of claim 15, wherein determining the composite sleep qualitymetric comprises determining an undisturbed sleep time metric.
 30. Themethod of claim 29, wherein the undisturbed sleep time metric, UST, ischaracterizable using the equation:UST=TA−STSD, where TA is associated with total time asleep and STSD isassociated with sleep time spent in sleep disorders.
 31. The method ofclaim 15, wherein determining the composite sleep quality metriccomprises determining an undisturbed sleep efficiency metric.
 32. Themethod of claim 31, wherein the undisturbed sleep efficiency metric,USE, is characterizable using the equation:USE=100*(TA−STSD)/TIB, where TA is associated with total time asleep,STSD is associated with sleep time in sleep disorders, and TIB isassociated with time in bed.
 33. The method of claim 15, furthercomprising trending at least one of the one or more metrics associatedwith sleep, the one or more metrics associated with events that disruptsleep, and the composite sleep quality metric over time.
 34. The methodof claim 15, further comprising using at least one of the one or moremetrics associated with sleep, the one or more metrics associated withevents that disrupt sleep, and the composite sleep quality metric foradvanced patient management.
 35. A method for assessing sleep quality ofa patient, comprising: detecting physiological and non-physiologicalconditions associated with the sleep quality of the patient; collectingsleep quality data based on the detected conditions; and evaluating thesleep quality of the patient using the collected data, wherein at leastone of collecting the sleep quality data and evaluating the sleepquality is performed at least in part implantably.
 36. The method ofclaim 35, wherein both collecting the sleep quality data and evaluatingthe sleep quality are performed at least in part implantably.
 37. Themethod of claim 35, wherein evaluating the sleep quality comprisesdetermining one or more sleep stages.
 38. The method of claim 35,wherein evaluating the sleep quality comprises detecting eventsassociated with sleep disruption.
 39. The method of claim 38, whereindetecting the events associated with sleep disruption comprisesdetecting disordered breathing.
 40. The method of claim 38, whereindetecting the events associated with sleep disruption comprisesdetecting movement disorders.
 41. The method of claim 35, whereinevaluating the sleep quality comprises determining one or more metricsassociated with sleep quality.
 42. The method of claim 35, whereinevaluating the sleep quality comprises trending one or more metricsassociated with sleep quality over time.
 43. The method of claim 35,wherein evaluating the sleep quality comprises determining one or moremetrics associated with disordered breathing.
 44. The method of claim35, wherein evaluating the sleep quality comprises determining one ormore metric associated with movement disorders.
 45. The method of claim35, wherein evaluating the sleep quality comprises determining one ormore composite metrics based on metrics associated with sleep andmetrics associated with events that disrupt sleep.
 46. The method ofclaim 35, further comprising transmitting at least one of the sleepquality data and the sleep quality evaluation to a separate device. 47.A method for evaluating sleep quality, comprising: detecting one or moreconditions associated with the sleep quality of a patient during aperiod of wakefulness; collecting sleep quality data based on thedetected one or more conditions; and evaluating the sleep quality of thepatient using the collected sleep quality data, wherein at least one ofcollecting the sleep quality data and evaluating the sleep quality isperformed at least in part implantably.
 48. The method of claim 47,wherein detecting the one or more conditions comprises detecting aphysiological condition.
 49. The method of claim 47, wherein detectingthe one or more conditions comprises detecting a non-physiologicalcondition.
 50. The method of claim 47, wherein detecting the one or moreconditions comprises detecting a nervous system condition.
 51. Themethod of claim 47, wherein detecting the one or more conditionscomprises detecting a cardiovascular system condition.
 52. The method ofclaim 47, wherein detecting the one or more conditions comprisesdetecting patient activity; and collecting the sleep quality datacomprises collecting data associated with the patient activity duringthe period of wakefulness.
 53. The method of claim 47, furthercomprising storing the collected sleep quality data.
 54. The method ofclaim 47, further comprising transmitting the collected sleep qualitydata.
 55. The method of claim 47, wherein evaluating the sleep qualitycomprises determining one or more sleep quality metrics.
 56. The methodof claim 47, further comprising transmitting at least one of the sleepquality data and the sleep quality evaluation to a separate device. 57.A medical device, comprising: a detector system configured to detectphysiological and non-physiological conditions associated with sleepquality of a patient; and a data collection system coupled to thedetector system and configured to collect sleep quality data based onthe detected conditions, wherein the data collection system includes animplantable component.
 58. The device of claim 57, wherein the detectorsystem comprises a patient-internal component.
 59. The device of claim57, wherein the detector system comprises a patient-external component.60. The device of claim 57, wherein the detector system comprises apatient input device.
 61. The device of claim 57, wherein the detectorsystem comprises a wirelessly coupled device.
 62. The device of claim57, wherein the detector system comprises a network accessible device.63. The device of claim 57, wherein the data collection system isconfigured to collect data associated with sleep stages.
 64. The deviceof claim 57, wherein the data collection system is configured to collectdata associated with events that disrupt sleep.
 65. The device of claim57, wherein the data collection system is configured to collect dataassociated with disordered breathing.
 66. The device of claim 57,wherein the data collection system is configured to collect dataassociated with a movement disorder.
 67. The device of claim 57, furthercomprising a memory unit configured to store the collected data.
 68. Thedevice of claim 57, further comprising a communication unit configuredto transmit the collected data.
 69. The device of claim 68, wherein thecommunication unit is configured to transmit the collected data over awireless link.
 70. The device of claim 68, wherein the communicationunit is configured to transmit the collected data to a remote device.71. The device of claim 57, wherein the device is configured to beimplemented as a component of an advanced patient management system. 72.A sleep quality evaluation device, comprising: a detector systemconfigured to detect physiological and non-physiological conditionsaffecting sleep quality of a patient; and a sleep quality processorcoupled to the detector system and configured to determine one or moremetrics associated with sleep and one or more metrics associated withevents that disrupt sleep, the sleep quality processor furtherconfigured to generate at least one composite sleep quality metric basedon the one or more metrics associated with sleep and the one or moremetrics associated with events that disrupt sleep.
 73. The device ofclaim 72, wherein at least one of the detector system and the sleepquality processor includes an implantable component.
 74. The device ofclaim 72, wherein both the detector system and the sleep qualityprocessor include an implantable component.
 75. The device of claim 72,wherein the one or more metrics associated with events that disruptsleep characterize a sleep disorder.
 76. The device of claim 72, whereinthe one more metrics associated with sleep characterize arousals. 77.The device of claim 72, wherein the at least one composite metriccharacterizes undisturbed sleep time.
 78. The device of claim 72,wherein the sleep quality processor is configured to trend over time atleast one of the one or more metrics associated with sleep, the one ormore metrics associated with events that disrupt sleep, and the at leastone composite sleep quality metric.
 79. The device of claim 72, whereinthe device is configured to be used as a component of an advancedpatient management system.
 80. A medical device for evaluating sleepquality, comprising: a detector system configured to detectphysiological and non-physiological conditions associated with sleepquality of a patient; a data collection system coupled to the detectorsystem and configured to collect sleep quality data based on thedetected conditions; and a data analysis system coupled to the datacollection system and configured to evaluate the sleep quality using thecollected sleep quality data, wherein at least one of the the datacollection system and the data analysis system include an implantablecomponent.
 81. The device of claim 80, wherein both of the the datacollection system and the data analysis system include an implantablecomponent.
 82. The device of claim 80, wherein the data analysis systemis configured to detect sleep stages.
 83. The device of claim 80,wherein the data analysis system is configured to detect one or moresleep disorders.
 84. The device of claim 83, wherein the one or moresleep disorders comprise disordered breathing.
 85. The device of claim83, wherein the one or more sleep disorders comprise a movementdisorder.
 86. The device of claim 80, wherein the data analysis systemis configured to determine one or more metrics associated with sleepquality.
 87. The device of claim 80, wherein the data analysis system isconfigured to trend the sleep quality data over time.
 88. The device ofclaim 80, further comprising a communication system configured totransmit at least one of the collected sleep quality data and the sleepquality evaluation to a separate device.
 89. The device of claim 80,wherein the device is configured to be used as a component in anadvanced patient management system.
 90. A system for collecting sleepquality data, comprising: means for detecting physiological andnon-physiological conditions associated with sleep quality of a patient;and means for collecting sleep quality data based on the detectedconditions, wherein the means for collecting includes an implantablecomponent.
 91. The system of claim 90, further comprising means forstoring the collected sleep quality data.
 92. The system of claim 90,further comprising means for transmitting the collected sleep qualitydata.
 93. A system for assessing sleep quality, comprising: means fordetermining one or more metrics associated with sleep; means fordetermining one or more metrics associated with events that disruptsleep; and means for determining a composite sleep quality metric as afunction of the one or more metrics associated with sleep and the one ormore metrics associated with events that disrupt sleep.
 94. The systemof claim 93, further comprising means for trending at least one of theone or more metrics associated with sleep, the one or more metricsassociated with events that disrupt sleep, and the composite sleepquality metric over time.
 95. A system for assessing sleep quality of apatient, comprising: means for detecting physiological andnon-physiological conditions associated with the sleep quality of thepatient; means for collecting sleep quality data associated with thesleep quality of the patient based on the detected conditions; and meansfor evaluating the sleep quality of the patient using the collecteddata, wherein at least one of the means for collecting and the means forevaluating include an implantable component.
 96. A sleep qualityevaluation system, comprising: means for detecting one or moreconditions associated with the sleep quality of a patient during aperiod of wakefulness; means for collecting sleep quality data based onthe one or more conditions; and means for evaluating the sleep qualityof the patient using the collected sleep quality data, wherein at leastone of the means for collecting the data and the means for evaluatingthe sleep quality comprises an implantable component.